Image representation is critical for many visual tasks. Instead of
representing images discretely with 2D arrays of pixels, a recent study, namely
local implicit image function (LIIF), denotes images as a continuous function
where pixel values are expansion by using the corresponding coordinates as
inputs. Due to its continuous nature, LIIF can be adopted for arbitrary-scale
image super-resolution tasks, resulting in a single effective and efficient
model for various up-scaling factors. However, LIIF often suffers from
structural distortions and ringing artifacts around edges, mostly because all
pixels share the same model, thus ignoring the local properties of the image.
In this paper, we propose a novel adaptive local image function (A-LIIF) to
alleviate this problem. Specifically, our A-LIIF consists of two main
components: an encoder and a expansion network. The former captures cross-scale
image features, while the latter models the continuous up-scaling function by a
weighted combination of multiple local implicit image functions. Accordingly,
our A-LIIF can reconstruct the high-frequency textures and structures more
accurately. Experiments on multiple benchmark datasets verify the effectiveness
of our method. Our codes are available at
https://github.com/LeeHW-THU/A-LIIF.

本研究提出了一种新的自适应局部图像函数（A-LIIF）模型，旨在通过采用编码器和扩展网络，建模多个局部隐式图像函数的加权组合，从而缓解局部隐式图像函数（LIIF）模型在边缘周围出现结构失真和环状伪影等问题，实现图像超分辨率的高精度重建。